{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "哈希结果：00288dac5a48bc7d986793ac2de80675127ec0b80a2e929f7c284d96112c7a29\n",
      "nonce值：10220，碰撞次数：221\n"
     ]
    }
   ],
   "source": [
    "import hashlib\n",
    "\n",
    "def minging():\n",
    "    base_string = \"区块链\"\n",
    "    nonce = 10000\n",
    "    count = 0\n",
    "    while True:\n",
    "        target_string = base_string + str(nonce)\n",
    "        pow_hash = hashlib.sha256(target_string.encode('utf8')).hexdigest()\n",
    "        count += 1\n",
    "        if pow_hash.startswith(\"00\"):\n",
    "            print(\"哈希结果：{}\".format(pow_hash))\n",
    "            print(\"nonce值：{}，碰撞次数：{}\".format(nonce,count))\n",
    "            break\n",
    "        nonce += 1\n",
    "minging()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最高患病人数为2325.886726929051人,达到峰值的时间是87天\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "T = 200 # 总天数\n",
    "N = 10000 # 总人数\n",
    "E = np.zeros(T) # 潜伏者人数\n",
    "I = np.ones(T) # 感染者人数\n",
    "S = np.full(T,N-1) # 易感人数\n",
    "R = np.zeros(T) # 康复人数\n",
    "\n",
    "r = 25 # 感染者接触易感者的人数\n",
    "beta = 0.02 # 传染概率\n",
    "alpha = 0.1 # 潜伏者转化为感染者概率\n",
    "gamma = 0.1 # 康复概率\n",
    "\n",
    "for i in range(T-1):\n",
    "    S[i+1] = S[i] - r*beta*S[i]*I[i]/N\n",
    "    E[i+1] = E[i] + r*beta*S[i]*I[i]/N - alpha*E[i]\n",
    "    I[i+1] = I[i] + alpha*E[i] - gamma*I[i]\n",
    "    R[i+1] = R[i] + gamma*I[i]\n",
    "print(\"最高患病人数为{}人,达到峰值的时间是{}天\".format(np.max(I),np.argmax(I)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-5-bf3ee5f30be8>:8: MatplotlibDeprecationWarning: Case-insensitive properties were deprecated in 3.3 and support will be removed two minor releases later\n",
      "  plt.xlabel(\"天数\",FontProperties = 'SimHei')\n",
      "<ipython-input-5-bf3ee5f30be8>:9: MatplotlibDeprecationWarning: Case-insensitive properties were deprecated in 3.3 and support will be removed two minor releases later\n",
      "  plt.ylabel(\"人数\",FontProperties = 'SimHei')\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.figure()\n",
    "plt.plot(S,label = \"易感人群\")\n",
    "plt.plot(E,label = \"潜伏者人群\")\n",
    "plt.plot(I,label = \"患病者人群\")\n",
    "plt.plot(R,label = \"康复者人群\")\n",
    "plt.xlabel(\"天数\",FontProperties = 'SimHei')\n",
    "plt.ylabel(\"人数\",FontProperties = 'SimHei')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最高患病人数为3215.759739721946人,达到峰值的时间是48天\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-8-83624f81a812>:31: MatplotlibDeprecationWarning: Case-insensitive properties were deprecated in 3.3 and support will be removed two minor releases later\n",
      "  plt.xlabel(\"天数\",FontProperties = 'SimHei')\n",
      "<ipython-input-8-83624f81a812>:32: MatplotlibDeprecationWarning: Case-insensitive properties were deprecated in 3.3 and support will be removed two minor releases later\n",
      "  plt.ylabel(\"人数\",FontProperties = 'SimHei')\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "T = 200 # 总天数\n",
    "N = 10000 # 总人数\n",
    "E = np.zeros(T) # 潜伏者人数\n",
    "I = np.ones(T) # 感染者人数\n",
    "S = np.full(T,N-1) # 易感人数\n",
    "R = np.zeros(T) # 康复人数\n",
    "\n",
    "r = 25 # 感染者接触易感者的人数\n",
    "r2 = 25 # 潜伏者接触易感者的人数\n",
    "beta = 0.02 # 传染概率\n",
    "beta2 = 0.01 # \n",
    "alpha = 0.1 # 潜伏者转化为感染者概率\n",
    "gamma = 0.1 # 康复概率\n",
    "\n",
    "for i in range(T-1):\n",
    "    S[i+1] = S[i] - r*beta*S[i]*I[i]/N - r2*beta2*S[i]*E[i]/N\n",
    "    E[i+1] = E[i] + r*beta*S[i]*I[i]/N - alpha*E[i]+r2*beta2*S[i]*E[i]/N\n",
    "    I[i+1] = I[i] + alpha*E[i] - gamma*I[i]\n",
    "    R[i+1] = R[i] + gamma*I[i]\n",
    "print(\"最高患病人数为{}人,达到峰值的时间是{}天\".format(np.max(I),np.argmax(I)))\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.figure()\n",
    "plt.plot(S,label = \"易感人群\")\n",
    "plt.plot(E,label = \"潜伏者人群\")\n",
    "plt.plot(I,label = \"患病者人群\")\n",
    "plt.plot(R,label = \"康复者人群\")\n",
    "plt.xlabel(\"天数\",FontProperties = 'SimHei')\n",
    "plt.ylabel(\"人数\",FontProperties = 'SimHei')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最高患病人数为106.58175277807729人,达到峰值的时间是199天\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-16-5f587f31f9d5>:37: MatplotlibDeprecationWarning: Case-insensitive properties were deprecated in 3.3 and support will be removed two minor releases later\n",
      "  plt.xlabel(\"天数\",FontProperties = 'SimHei')\n",
      "<ipython-input-16-5f587f31f9d5>:38: MatplotlibDeprecationWarning: Case-insensitive properties were deprecated in 3.3 and support will be removed two minor releases later\n",
      "  plt.ylabel(\"人数\",FontProperties = 'SimHei')\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "T = 200 # 总天数\n",
    "N = 10000 # 总人数\n",
    "E = np.zeros(T) # 潜伏者人数\n",
    "I = np.ones(T) # 感染者人数\n",
    "S = np.full(T,N-1) # 易感人数\n",
    "R = np.zeros(T) # 康复人数\n",
    "D = np.zeros(T) # 死亡人数\n",
    "\n",
    "r = 25 # 感染者接触易感者的人数\n",
    "r2 = 25 # 潜伏者接触易感者的人数\n",
    "beta = 0.02 # 传染概率\n",
    "beta2 = 0.01 # \n",
    "alpha = 0.1 # 潜伏者转化为感染者概率\n",
    "gamma = 0.1 # 康复概率\n",
    "died = 0.1\n",
    "for i in range(T-1):\n",
    "    if i+1>=15:\n",
    "        r = 6\n",
    "        r2 = 6\n",
    "        beta = 0.01\n",
    "        beta2 = 0.01\n",
    "    S[i+1] = S[i] - r*beta*S[i]*I[i]/N - r2*beta2*S[i]*E[i]/N\n",
    "    E[i+1] = E[i] + r*beta*S[i]*I[i]/N - alpha*E[i]+r2*beta2*S[i]*E[i]/N\n",
    "    I[i+1] = I[i] + alpha*E[i] - gamma*I[i]\n",
    "    R[i+1] = R[i] + gamma*I[i]\n",
    "print(\"最高患病人数为{}人,达到峰值的时间是{}天\".format(np.max(I),np.argmax(I)))\n",
    "print()\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.figure()\n",
    "plt.plot(S,label = \"易感人群\")\n",
    "plt.plot(E,label = \"潜伏者人群\")\n",
    "plt.plot(I,label = \"患病者人群\")\n",
    "plt.plot(R,label = \"康复者人群\")\n",
    "plt.xlabel(\"天数\",FontProperties = 'SimHei')\n",
    "plt.ylabel(\"人数\",FontProperties = 'SimHei')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "个数排在第1的单词和其个数分别为：the:1143\n",
      "个数排在第2的单词和其个数分别为：and:966\n",
      "个数排在第3的单词和其个数分别为：to:762\n",
      "个数排在第4的单词和其个数分别为：of:669\n",
      "个数排在第5的单词和其个数分别为：i:631\n",
      "个数排在第6的单词和其个数分别为：you:554\n",
      "个数排在第7的单词和其个数分别为：a:546\n",
      "个数排在第8的单词和其个数分别为：my:514\n",
      "个数排在第9的单词和其个数分别为：hamlet:471\n",
      "个数排在第10的单词和其个数分别为：in:451\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import re\n",
    "# inport jieba\n",
    "\n",
    "# 打开文件并读取内容\n",
    "with open('./hamlet.txt', 'r') as file:\n",
    "    content = file.read().lower()    \n",
    "    newcontent = re.sub(r'[^\\w\\s]',' ',content).split()\n",
    "count = {}\n",
    "# 对于筛选过后的数据加上value值并将其转换为数组\n",
    "for word in newcontent:\n",
    "    count[word] = count.get(word,0)+1\n",
    "#字典的.get()用来从字典中获得某一个键对应的值\n",
    "#若word在字典里面，则返回其次数再加1；若不在，则将其加入字典，赋给其0再加1\n",
    "#统计完后，将字典类型转变为列表类型\n",
    "items = list(count.items())\n",
    "# 进行排序 根据value值进行排序\n",
    "items.sort(key=lambda x:x[1],reverse=True)\n",
    "# print(items)\n",
    "x = []\n",
    "y = []\n",
    "for i in range(10):\n",
    "    word,count=items[i]\n",
    "    print(\"个数排在第{}的单词和其个数分别为：{}:{}\".format(i+1,word,count))\n",
    "    x.append(word)\n",
    "    y.append(count)\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.figure()\n",
    "plt.title(\"哈姆雷特文章中排名前十的词汇和个数\")\n",
    "plt.bar(x,y)\n",
    "plt.xlabel(\"词汇\")\n",
    "plt.ylabel(\"个数\")\n",
    "# plt.legend()\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "个数排在第1的单词和其个数分别为：the:1143\n",
      "个数排在第2的单词和其个数分别为：and:966\n",
      "个数排在第3的单词和其个数分别为：to:762\n",
      "个数排在第4的单词和其个数分别为：of:669\n",
      "个数排在第5的单词和其个数分别为：i:631\n",
      "个数排在第6的单词和其个数分别为：you:554\n",
      "个数排在第7的单词和其个数分别为：a:546\n",
      "个数排在第8的单词和其个数分别为：my:514\n",
      "个数排在第9的单词和其个数分别为：hamlet:471\n",
      "个数排在第10的单词和其个数分别为：in:451\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import re\n",
    "# inport jieba\n",
    "\n",
    "# 打开文件并读取内容\n",
    "with open('./hamlet.txt', 'r') as file:\n",
    "    content = file.read().lower()    \n",
    "    newcontent = re.sub(r'[^\\w\\s]',' ',content).split()\n",
    "count = {}\n",
    "# 对于筛选过后的数据加上value值并将其转换为数组\n",
    "for word in newcontent:\n",
    "    count[word] = count.get(word,0)+1\n",
    "#字典的.get()用来从字典中获得某一个键对应的值\n",
    "#若word在字典里面，则返回其次数再加1；若不在，则将其加入字典，赋给其0再加1\n",
    "#统计完后，将字典类型转变为列表类型\n",
    "items = list(count.items())\n",
    "# 进行排序 根据value值进行排序\n",
    "items.sort(key=lambda x:x[1],reverse=True)\n",
    "# print(items)\n",
    "x = []\n",
    "y = []\n",
    "for i in range(10):\n",
    "    word,count=items[i]\n",
    "    print(\"个数排在第{}的单词和其个数分别为：{}:{}\".format(i+1,word,count))\n",
    "    x.append(word)\n",
    "    y.append(count)\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.figure()\n",
    "plt.title(\"哈姆雷特文章中排名前十的词汇和个数\")\n",
    "plt.bar(x,y)\n",
    "plt.xlabel(\"词汇\")\n",
    "plt.ylabel(\"个数\")\n",
    "# plt.legend()\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
